Honolulu, Hawaii –Hoag Neurosciences Institute (HNI)announced encouraging study results for a new analytical approach that can ultimately improve the analysis of clinical trials’ data forAlzheimer’s disease at the American Academy of Neurology annual meeting in Honolulu, HI. The study results concluded that the hierarchical Bayesian cognitive processing models (HBCP) can not only be applied to much smaller samples to identify treatment effects not detected by traditional methods applied to larger samples, but also provide much precise measurement of cognitive changes. This approach can be also applied to trials of other disorders and to re-analyses of previous clinical trials.

Standard analytic methods for clinical trial outcomes can be affected by small samples, missing data, distribution assumptions, and by the use of test scores that are not optimally scaled. The study found that HBCP models resolved these small sample problems and detected a harmful effect for a class of drugs that are currently being tested in FDA clinical trials by other pharmaceutical companies. The drug tested (Flurizan®, Myriad Pharmaceuticals) belongs to the gamma secretase inhibitor class of drugs, and was found to harm memory storage. This harmful effect was missed by standard analytical methods applied to the full sample. The harmful effect was also consistent with the greater decline in dementia severity seen in Flurizan-treated patients, and with another phase III gamma secretase inhibitor trial's findings.

“An important ethical issue is whether these more powerful analytical methods should be applied to phase I and II FDA clinical trials to avoid exposing the much larger samples of individuals tested in phase III FDA trials to potentially harmful treatments,” saidWilliam R. Shankle, MS, MD, FACP, program director,Memory and Cognitive Disorders,Hoag Neurosciences Institute. “Our study showed that Flurizan’s harmful effect on memory persisted after the drug was discontinued. If HBCP methods had been applied to the results of the phase I or II FDA Flurizan trials, it may have avoided exposing the much larger group of Alzheimer’s patients that were treated in the phase III trial to this potentially permanent harmful effect on memory.”

The study, entitled “Application of Hierarchical Bayesian Cognitive Processing Models in Clinical Trials for Alzheimer’s Disease,” lead authors are William R. Shankle and Michael D. Lee, professor in the department of cognitive sciences at the University of California, Irvine. The study was supported by the Alzheimer’s Association and Medical Care Corporation.

“While nothing can be done about the past, there are other ongoing FDA trials of drugs that can affect cognition, whose phase I or II results could be examined to make sure the public is protected from potentially harmful effects on cognition that were missed by the methods currently used,” adds Dr. Shankle. “The other side of this issue is that potentially beneficial drugs may be prematurely discontinued if the standard analytical methods used do not detect beneficial effects. Our findings demonstrate that HBCP modeling methods can be applied to other clinical trials in which the treatment may affect cognition, so that the public is better protected and that useful treatments are not missed.”

HBCP models were applied to the ADAS-Cog (Alzheimer Disease Assessment Scale-Cognitive) and MCI Screen (MCIS) wordlist memory (WLM) item response data from the phase III gamma-secretase inhibitor trial (Flurizan®, Myriad Pharmaceuticals). The standard methods applied to the full sample of 1,639 AD patients found no treatment group difference using the ADAS-Cog test, but Flurizan patients declined more in dementia severity using the Clinical Dementia Rating Scale item scores. Data were available for a subset of 14 patients (6 Flurizan, 8 placebo). The HBCP model estimated underlying processes of memory performance for the MCIS and ADAS-Cog tests. Results were adjusted for number of previous WLM assessments, and test used (ADAS-Cog vs. MCIS).

“We have found that Hierarchical Bayesian methods provide a flexible and interpretable way of extending simple models of cognitive processes,” said Lee. “They provide a compelling and influential framework for representing and processing information. Over the last few decades, it has become the major approach in the field of statistics, and has come to be accepted in many or most of the physical, biological and human sciences.”